Production facilities, commonly referred to as plants, continue to increase in complexity due to automation and interconnections between sections of the plant, individual machines within the plant and interconnections between different layers of the “automation pyramid”, among other reasons. Typically the “automation pyramid” is classified into the following layers: the ERP-layer (Enterprise Resource Planning), the MES-layer (Manufacturing Execution System) and the controls-layer. Coping with this increasing complexity is difficult for predictive error recognition corrective and precautionary maintenance activities. This affects the operator of the plant, the original equipment manufacturer (OEM), and the system and component supplier.
U.S. Pat. No. 6,487,404 discloses a system and method of detecting radio network trends in a telecommunications network using a data mining tool. But this invention does not disclose the usage of a data mining tool for predictive error recognition in manufacturing systems.
German patent application DE 199 59 526 A1 discloses a method for predictive error recognition in a vehicle.
Japanese patent JP 10255091 A discloses a system to perform predicted maintenance using statistical data.
US PAP 2001/0037363 A1 discloses another method for providing consulting services to resolve a problem in a centralized web-based environment.
U.S. Pat. No. 5,311,562 discloses an integrated information system for plants such as nuclear power generation plants for maintenance with predictive diagnostics. But this invention needs additional sensors to monitor the processes in the plant.
In the past two types of systems and methods have been established for predictive error recognition. On the first hand, systems and methods which use technological models, based on inherent plant data. These model based systems and methods have the following disadvantages: the models require additional engineering effort, the quality of a model based predictive error recognition depends on the quality of the transformation of the plant facts into the models and only errors which are thought ahead are able to be predicted.
On the other hand, systems and methods which use dedicated sensors for predictive statements can be used for predictive error recognition. This approach has the following disadvantages: also additional engineering efforts are necessary and in the plant is additional wiring necessary to mount and connect the sensors.
Therefore there is a need for a method and a system predictive error recognition which do not require additional engineering efforts and which do avoid additional hardware and wiring in the shop floor.